OpenRaiser/Intern-Atlas huggingface dataset:
Differential Privacy on Finite Computers
Venue: arXiv cs.DS 2019
Year: 2019

We consider the problem of designing and analyzing differentially private algorithms that can be implemented on {\em discrete} models of computation in {\em strict} polynomial time, motivated by known attacks on floating point implementations of real-arithmetic differentially private algorithms (Mironov, CCS 2012) and the potential for timing attacks on expected polynomial-time algorithms. As a case study, we examine the basic problem of approximating the histogram of a categorical dataset over a possibly large data universe $\mathcal{X}$. The classic Laplace Mechanism (Dwork, McSherry, Nissim, Smith, TCC 2006 and J. Privacy \& Confidentiality 2017) does not satisfy our requirements, as it is based on real arithmetic, and natural discrete analogues, such as the Geometric Mechanism (Ghosh, Roughgarden, Sundarajan, STOC 2009 and SICOMP 2012), take time at least linear in $|\mathcal{X}|$, which can be exponential in the bit length of the input. In this paper, we provide strict polynomial-time discrete algorithms for approximate histograms whose simultaneous accuracy (the maximum error over all bins) matches that of the Laplace Mechanism up to constant factors, while retaining the same (pure) differential privacy guarantee. One of our algorithms produces a sparse histogram as output. Its "per-bin accuracy" (the error on individual bins) is worse than that of the Laplace Mechanism by a factor of $\log|\mathcal{X}|$, but we prove a lower bound showing that this is necessary for any algorithm that produces a sparse histogram. A second algorithm avoids this lower bound, and matches the per-bin accuracy of the Laplace Mechanism, by producing a compact and efficiently computable representation of a dense histogram, it is based on an $(n+1)$-wise independent implementation of an appropriately clamped version of the Discrete Geometric Mechanism.
FlashProfile: A Framework for Synthesizing Data Profiles
Venue: arXiv cs.LG 2019
Year: 2019

We address the problem of learning a syntactic profile for a collection of strings, i.e. a set of regex-like patterns that succinctly describe the syntactic variations in the strings. Real-world datasets, typically curated from multiple sources, often contain data in various syntactic formats. Thus, any data processing task is preceded by the critical step of data format identification. However, manual inspection of data to identify the different formats is infeasible in standard big-data scenarios. Prior techniques are restricted to a small set of pre-defined patterns (e.g. digits, letters, words, etc.), and provide no control over granularity of profiles. We define syntactic profiling as a problem of clustering strings based on syntactic similarity, followed by identifying patterns that succinctly describe each cluster. We present a technique for synthesizing such profiles over a given language of patterns, that also allows for interactive refinement by requesting a desired number of clusters. Using a state-of-the-art inductive synthesis framework, PROSE, we have implemented our technique as FlashProfile. Across $153$ tasks over $75$ large real datasets, we observe a median profiling time of only $\sim\,0.7\,$s. Furthermore, we show that access to syntactic profiles may allow for more accurate synthesis of programs, i.e. using fewer examples, in programming-by-example (PBE) workflows such as FlashFill.
Non-iterative Label Propagation in Optimal Leading Forest
Venue: arXiv cs.LG 2019
Year: 2019

Graph based semi-supervised learning (GSSL) has intuitive representation and can be improved by exploiting the matrix calculation. However, it has to perform iterative optimization to achieve a preset objective, which usually leads to low efficiency. Another inconvenience lying in GSSL is that when new data come, the graph construction and the optimization have to be conducted all over again. We propose a sound assumption, arguing that: the neighboring data points are not in peer-to-peer relation, but in a partial-ordered relation induced by the local density and distance between the data; and the label of a center can be regarded as the contribution of its followers. Starting from the assumption, we develop a highly efficient non-iterative label propagation algorithm based on a novel data structure named as optimal leading forest (LaPOLeaF). The major weaknesses of the traditional GSSL are addressed by this study. We further scale LaPOLeaF to accommodate big data by utilizing block distance matrix technique, parallel computing, and Locality-Sensitive Hashing (LSH). Experiments on large datasets have shown the promising results of the proposed methods.
Combinatorial Optimization of AC Optimal Power Flow with Discrete Demands in Radial Networks
Venue: arXiv cs.DS 2019
Year: 2019

The AC Optimal power flow (OPF) problem is one of the most fundamental problems in power systems engineering. For the past decades, researchers have been relying on unproven heuristics to tackle OPF. The hardness of OPF stems from two issues: (1) non-convexity and (2) combinatoric constraints (e.g., discrete power extraction constraints). The recent advances in providing sufficient conditions on the exactness of convex relaxation of OPF can address the issue of non-convexity. To complete the understanding of OPF, this paper presents a polynomial-time approximation algorithm to solve the convex-relaxed OPF with discrete demands as combinatoric constraints, which has a provably small parameterized approximation ratio (also known as PTAS algorithm). Together with the sufficient conditions on the exactness of the convex relaxation, we provide an efficient approximation algorithm to solve OPF with discrete demands, when the underlying network is radial with a fixed size and one feeder. The running time of PTAS is $O(n^{4m/ε}T)$, where $T$ is the time required to solve a convex relaxation of the problem, and $m, ε$ are fixed constants. Based on prior hardness results of OPF, our PTAS is among the best achievable in theory. Simulations show our algorithm can produce close-to-optimal solutions in practice.
A polynomial-time approximation algorithm for all-terminal network reliability
Venue: arXiv cs.DS 2019
Year: 2019

We give a fully polynomial-time randomized approximation scheme (FPRAS) for the all-terminal network reliability problem, which is to determine the probability that, in a undirected graph, assuming each edge fails independently, the remaining graph is still connected. Our main contribution is to confirm a conjecture by Gorodezky and Pak (Random Struct. Algorithms, 2014), that the expected running time of the "cluster-popping" algorithm in bi-directed graphs is bounded by a polynomial in the size of the input.
The Consciousness Prior
Venue: arXiv cs.LG 2019
Year: 2019

A new prior is proposed for learning representations of high-level concepts of the kind we manipulate with language. This prior can be combined with other priors in order to help disentangling abstract factors from each other. It is inspired by cognitive neuroscience theories of consciousness, seen as a bottleneck through which just a few elements, after having been selected by attention from a broader pool, are then broadcast and condition further processing, both in perception and decision-making. The set of recently selected elements one becomes aware of is seen as forming a low-dimensional conscious state. This conscious state is combining the few concepts constituting a conscious thought, i.e., what one is immediately conscious of at a particular moment. We claim that this architectural and information-processing constraint corresponds to assumptions about the joint distribution between high-level concepts. To the extent that these assumptions are generally true (and the form of natural language seems consistent with them), they can form a useful prior for representation learning. A low-dimensional thought or conscious state is analogous to a sentence: it involves only a few variables and yet can make a statement with very high probability of being true. This is consistent with a joint distribution (over high-level concepts) which has the form of a sparse factor graph, i.e., where the dependencies captured by each factor of the factor graph involve only very few variables while creating a strong dip in the overall energy function. The consciousness prior also makes it natural to map conscious states to natural language utterances or to express classical AI knowledge in a form similar to facts and rules, albeit capturing uncertainty as well as efficient search mechanisms implemented by attention mechanisms.
Recognizing Weak Embeddings of Graphs
Venue: arXiv cs.CG 2019
Year: 2019

We present an efficient algorithm for a problem in the interface between clustering and graph embeddings. An embedding $φ:G\rightarrow M$ of a graph $G$ into a 2-manifold $M$ maps the vertices in $V(G)$ to distinct points and the edges in $E(G)$ to interior-disjoint Jordan arcs between the corresponding vertices. In applications in clustering, cartography, and visualization, nearby vertices and edges are often bundled to the same point or overlapping arcs, due to data compression or low resolution. This raises the computational problem of deciding whether a given map $φ:G\rightarrow M$ comes from an embedding. A map $φ:G\rightarrow M$ is a \textbf{weak embedding} if it can be perturbed into an embedding $ψ_\varepsilon:G\rightarrow M$ with $\|φ-ψ_\varepsilon\|<\varepsilon$ for every $\varepsilon>0$, where $\|.\|$ is the unform norm. A polynomial-time algorithm for recognizing weak embeddings has recently been found by Fulek and Kynčl. It reduces the problem to solving a system of linear equations over $\mathbb{Z}_2$. It runs in $O(n^{2ω})\leq O(n^{4.75})$ time, where $ω\in [2,2.373)$ is the matrix multiplication exponent and $n$ is the number of vertices and edges of $G$. We improve the running time to $O(n\log n)$. Our algorithm is also conceptually simpler: We perform a sequence of \emph{local operations} that gradually "untangles" the image $φ(G)$ into an embedding $ψ(G)$, or reports that $φ$ is not a weak embedding. It combines local constraints on the orientation of subgraphs directly, thereby eliminating the need for solving large systems of linear equations.
An improved algorithm for recognizing matroids
Venue: arXiv cs.CC 2019
Year: 2019

Let $M$ be a matroid defined on a finite set $E$ and $L\subset E$. $L$ is locked in $M$ if $M|L$ and $M^*|(E\backslash L)$ are 2-connected, and $min\{r(L), r^*(E\backslash L)\} \geq 2$. Locked subsets characterize nontrivial facets of the bases polytope. In this paper, we give a new axiom system for matroids based on locked subsets. We deduce an algorithm for recognizing matroids improving the running time complexity of the best known till today. This algorithm induces a polynomial time algorithm for recognizing uniform matroids. This latter problem is intractable if we use an independence oracle.
Cooperative Automated Vehicles: a Review of Opportunities and Challenges in Socially Intelligent Vehicles Beyond Networking
Venue: arXiv cs.AI 2019
Year: 2019

The connected automated vehicle has been often touted as a technology that will become pervasive in society in the near future. One can view an automated vehicle as having Artificial Intelligence (AI) capabilities, being able to self-drive, sense its surroundings, recognise objects in its vicinity, and perform reasoning and decision-making. Rather than being stand alone, we examine the need for automated vehicles to cooperate and interact within their socio-cyber-physical environments, including the problems cooperation will solve, but also the issues and challenges. We review current work in cooperation for automated vehicles, based on selected examples from the literature. We conclude noting the need for the ability to behave cooperatively as a form of social-AI capability for automated vehicles, beyond sensing the immediate environment and beyond the underlying networking technology.
Synchronizing Data Words for Register Automata
Venue: arXiv cs.FL 2019
Year: 2019

Register automata (RAs) are finite automata extended with a finite set of registers to store and compare data from an infinite domain. We study the concept of synchronizing data words in RAs: does there exist a data word that sends all states of the RA to a single state? For deterministic RAs with k registers (k-DRAs), we prove that inputting data words with 2k+1 distinct data from the infinite data domain is sufficient to synchronize. We show that the synchronization problem for DRAs is in general PSPACE-complete, and it is NLOGSPACE-complete for 1-DRAs. For nondeterministic RAs (NRAs), we show that Ackermann(n) distinct data (where n is the size of the RA) might be necessary to synchronize. The synchronization problem for NRAs is in general undecidable, however, we establish Ackermann-completeness of the problem for 1-NRAs. Another main result is the NEXPTIME-completeness of the length-bounded synchronization problem for NRAs, where a bound on the length of the synchronizing data word, written in binary, is given. A variant of this last construction allows to prove that the length-bounded universality problem for NRAs is co-NEXPTIME-complete.
Efficient Dynamic Dictionary Matching with DAWGs and AC-automata
Venue: arXiv cs.DS 2019
Year: 2019

The dictionary matching is a task to find all occurrences of patterns in a set $D$ (called a dictionary) on a text $T$. The Aho-Corasick-automaton (AC-automaton) is a data structure which enables us to solve the dictionary matching problem in $O(d\logσ)$ preprocessing time and $O(n\logσ+occ)$ matching time, where $d$ is the total length of the patterns in $D$, $n$ is the length of the text, $σ$ is the alphabet size, and $occ$ is the total number of occurrences of all the patterns in the text. The dynamic dictionary matching is a variant where patterns may dynamically be inserted into and deleted from $D$. This problem is called semi-dynamic dictionary matching if only insertions are allowed. In this paper, we propose two efficient algorithms. For a pattern of length $m$, our first algorithm supports insertions in $O(m\logσ+\log d/\log\log d)$ time and pattern matching in $O(n\logσ+occ)$ time for the semi-dynamic setting and supports both insertions and deletions in $O(σm+\log d/\log\log d)$ time and pattern matching in $O(n(\log d/\log\log d+\logσ)+occ(\log d/\log\log d))$ time for the dynamic setting by some modifications. This algorithm is based on the directed acyclic word graph. Our second algorithm, which is based on the AC-automaton, supports insertions in $O(m\log σ+u_f+u_o)$ time for the semi-dynamic setting and supports both insertions and deletions in $O(σm+u_f+u_o)$ time for the dynamic setting, where $u_f$ and $u_o$ respectively denote the numbers of states in which the failure function and the output function need to be updated. This algorithm performs pattern matching in $O(n\logσ+occ)$ time for both settings. Our algorithm achieves optimal update time for AC-automaton based methods over constant-size alphabets, since any algorithm which explicitly maintains the AC-automaton requires $Ω(m+u_f+u_o)$ update time.
Joint Weakly and Semi-Supervised Deep Learning for Localization and Classification of Masses in Breast Ultrasound Images
Venue: arXiv cs.CV 2019
Year: 2019

We propose a framework for localization and classification of masses in breast ultrasound (BUS) images. We have experimentally found that training convolutional neural network based mass detectors with large, weakly annotated datasets presents a non-trivial problem, while overfitting may occur with those trained with small, strongly annotated datasets. To overcome these problems, we use a weakly annotated dataset together with a smaller strongly annotated dataset in a hybrid manner. We propose a systematic weakly and semi-supervised training scenario with appropriate training loss selection. Experimental results show that the proposed method can successfully localize and classify masses with less annotation effort. The results trained with only 10 strongly annotated images along with weakly annotated images were comparable to results trained from 800 strongly annotated images, with the 95% confidence interval of difference -3.00%--5.00%, in terms of the correct localization (CorLoc) measure, which is the ratio of images with intersection over union with ground truth higher than 0.5. With the same number of strongly annotated images, additional weakly annotated images can be incorporated to give a 4.5% point increase in CorLoc, from 80.00% to 84.50% (with 95% confidence intervals 76.00%--83.75% and 81.00%--88.00%). The effects of different algorithmic details and varied amount of data are presented through ablative analysis.
Monotonicity axioms in approval-based multi-winner voting rules
Venue: arXiv cs.GT 2019
Year: 2019

In this paper we study several monotonicity axioms in approval-based multi-winner voting rules. We consider monotonicity with respect to the support received by the winners and also monotonicity in the size of the committee. Monotonicity with respect to the support is studied when the set of voters does not change and when new voters enter the election. For each of these two cases we consider a strong and a weak version of the axiom. We observe certain incompatibilities between the monotonicity axioms and well-known representation axioms (extended/proportional justified representation) for the voting rules that we analyze and provide formal proofs of incompatibility between some of these axioms and perfect representation.
Weak Memory Models with Matching Axiomatic and Operational Definitions
Venue: arXiv cs.PL 2019
Year: 2019

Memory consistency models are notorious for being difficult to define precisely, to reason about, and to verify. More than a decade of effort has gone into nailing down the definitions of the ARM and IBM Power memory models, and yet there still remain aspects of those models which (perhaps surprisingly) remain unresolved to this day. In response to these complexities, there has been somewhat of a recent trend in the (general-purpose) architecture community to limit new memory models to being (multicopy) atomic: where store values can be read by the issuing processor before being advertised to other processors. TSO is the most notable example, used in the past by IBM 370 and SPARC-TSO, and currently used in x86. Recently (in March 2017) ARM has also switched to a multicopy atomic memory model, and the new RISC-V ISA and recent academic proposals such as WMM are pushing to do the same. In this paper, we show that when memory models are atomic, it becomes much easier to produce axiomatic definitions, operational definitions, and proofs of equivalence than doing the same under non-atomic models. The increased ease with which these definitions can be produced in turn allows architects to build processors much more confidently, and yet the relaxed nature of the models we propose still allows most or all of the performance of non-atomic models to be retained. In fact, in this paper, we show that atomic memory models can be defined in a way that is parametrized by basic instruction and fence orderings. Our operational vs. axiomatic equivalence proofs, which are likewise parameterized, show that the operational model is sound with respect to the axioms and that the operational model is complete: that it can show any behavior permitted by axiomatic model.
Digital Currency Design for Sustainable Active Debris Removal in Space
Venue: arXiv cs.CY 2019
Year: 2019

Orbital debris remains as an obstacle to further space development. While efforts are ongoing to avoid newly launched objects becoming debris, the number of debris would still continue to grow because of collisions. ADR (Active Debris Removal) is an effective measure, but building a sustainable economic model for ADR remains as a difficult problem. We propose that the cost of removal can be paid by circulating digital currency tokens on a blockchain platform whose values may decrease and/or increase over time, issued by global cooperation (a consortium) of parties interested in space development, in exchange with proofs of ADR. The tokens pay their cost by themselves through contributions by the token holders, who are likely to be benefited by removal of debris. This scheme imposes virtually no cost to the consortium. We have generalized this concept as POD (Proof of Disposal), which, we believe, provides a more accountable foundation for solving social problems with digital currency than many ICO (Initial Coin or Cryptoasset Offering) in practice today. We evaluated the feasibility of our proposal through a simulation. We conclude that dynamic estimation of the economic values of each ADR and automated pricing of tokens that represent the orbital debris being removed are indeed possible. Actual prototyping of the proposed digital currency system is ongoing.
An Online Optimization Framework for Distributed Fog Network Formation with Minimal Latency
Venue: arXiv cs.IT 2019
Year: 2019

Fog computing is emerging as a promising paradigm to perform distributed, low-latency computation by jointly exploiting the radio and computing resources of end-user devices and cloud servers. However, the dynamic and distributed formation of local fog networks is highly challenging due to the unpredictable arrival and departure of neighboring fog nodes. Therefore, a given fog node must properly select a set of neighboring nodes and intelligently offload its computational tasks to this set of neighboring fog nodes and the cloud in order to achieve low-latency transmission and computation. In this paper, the problem of fog network formation and task distribution is jointly investigated while considering a hybrid fog-cloud architecture. The goal is to minimize the maximum computational latency by enabling a given fog node to form a suitable fog network and optimize the task distribution, under uncertainty on the arrival process of neighboring fog nodes. To solve this problem, a novel online optimization framework is proposed in which the neighboring nodes are selected by using a threshold-based online algorithm that uses a target competitive ratio, defined as the ratio between the latency of the online algorithm and the offline optimal latency. The proposed framework repeatedly updates its target competitive ratio and optimizes the distribution of the fog node's computational tasks in order to minimize latency. Simulation results show that the proposed framework can successfully select a set of neighboring nodes while reducing latency by up to 19.25% compared to a baseline approach based on the well-known online secretary framework. The results also show how, using the proposed framework, the computational tasks can be properly offloaded between the fog network and a remote cloud server in different network settings.
Fast Analog Beam Tracking in Phased Antenna Arrays: Theory and Performance
Venue: arXiv cs.IT 2019
Year: 2019

The directionality of millimeter-wave (mmWave) communications introduces a significant challenge in serving fast-rotating/moving terminals, e.g., mobile AR/VR, high-speed vehicles, trains, UAVs.This challenge is exacerbated in mmWave systems using analog beamforming, because of the inherent non-convexity in the analog beam tracking problem. In this paper, we obtain the Cramér-Rao lower bound (CRLB) of beam tracking and optimize the analog beamforming vectors to get the minimum CRLB. Then, we develop a low complexity analog beam tracking algorithm that simultaneously optimizes the analog beamforming vector and the estimate of beam direction. Finally, by establishing a new basic theory, we provide the theoretical convergence analysis of the proposed analog beam tracking algorithm, which proves that the minimum CRLB of the MSE is achievable with high probability. Our simulations show that this algorithm can achieve faster tracking speed, higher tracking accuracy and higher data rate than several state-of-the-art algorithms. The key analytical tools used in our algorithm design are stochastic approximation and recursive estimation with a control parameter.